Abstract
The software POPS performs inference of population genetic structure using multilocus genotypic data. Based on a hierarchical Bayesian framework for latent regression models, POPS implements algorithms that improve estimation of individual admixture proportions and cluster membership probabilities by using geographic and environmental information. In addition, POPS defines ancestry distribution models allowing its users to forecast admixture proportion and cluster membership geographic variation under changing environmental conditions. We illustrate a typical use of POPS using data for an alpine plant species, for which POPS predicts changes in spatial population structure assuming a particular scenario of climate change.
Highlights
Associations between population genetic structure and ecological variables have been frequently reported in the recent literature (Duminil et al 2007; Aitken, Yeaman, Holliday, Wang, and Curtis-McLane 2008; Sork et al 2010; Lee and Mitchell-Olds 2011)
Population genetic structure is commonly estimated by identifying genetic clusters defined as genetically divergent groups of individuals that arise from isolation of populations, and by computing individual membership probabilities for each genetic cluster (Davies, Villablanca, and Roderick 1999; Pritchard, Stephens, and Donnelly 2000)
Because genetic ancestry can be shared among several clusters, another way to estimate population structure is to infer admixture proportions representing the relative contributions of distinct ancestral populations to a genome
Summary
Associations between population genetic structure and ecological variables have been frequently reported in the recent literature (Duminil et al 2007; Aitken, Yeaman, Holliday, Wang, and Curtis-McLane 2008; Sork et al 2010; Lee and Mitchell-Olds 2011). The first efforts to infer genetic clusters and individual admixture proportions using Bayesian modeling date back to the introduction of the computer programs STRUCTURE and PARTITION (Pritchard et al 2000; Dawson and Belkhir 2001). We introduce POPS, a software that implements Bayesian clustering algorithms based on genetic, geographic and environmental variables. The principle of POPS is that individuals sharing similar environmental conditions and geographically close to each other are likely to share genetic ancestry. To achieve this objective POPS assigns individuals or genes to genetic groups after modeling the effects of geography and environment on individual membership and admixture proportions.
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